An Improved Model of Product Classification Feature Extraction and Recognition Based on Intelligent Image Recognition

Area of Science:

• Manufacturing Technology
• Artificial Intelligence
• Computer Vision

Background:

• The manufacturing industry is evolving towards intelligent manufacturing, demanding higher precision in product customization and small-batch production.
• Current intelligent image recognition technologies face limitations in analyzing complex product requirements for special or small-batch items.
• Accurate product classification is crucial for meeting personalized customization needs in modern manufacturing.

Purpose of the Study:

• To develop an improved model for product classification feature extraction and recognition using intelligent image recognition.
• To enhance the analysis and recognition capabilities for special customized and complex small-batch products.
• To achieve a recognition accuracy of 90% or higher for real-world production requirements.

Main Methods:

• 3D modeling of target products to analyze and record model data.
• Simulation of product parameters in real-world scenarios using 3D models and specialized tools.
• Application of image detection, edge analysis, and cross-validation algorithms to maximize parameter accuracy.
• Development of a data platform for comparing simulated and real-world data using software, algorithms, and cloud computing.

Main Results:

• The proposed algorithm demonstrates high accuracy in product classification and feature extraction.
• The developed model successfully simulates and analyzes product parameters, achieving close alignment with real-world product data.
• Cross-validation confirmed the algorithm's correct rate meets the standard of 90% and above.

Conclusions:

• The enhanced intelligent image recognition model effectively addresses the limitations of current technologies for complex product classification.
• The study's findings indicate the algorithm's suitability for diverse classification needs in actual intelligent manufacturing production.
• The research contributes to advancing intelligent manufacturing by improving the precision and efficiency of product recognition and customization.